
import numpy as np
import pandas as pd

import seaborn as sns #数据可视化
import matplotlib.pyplot as plt
%matplotlib inline
from ydata_profiling import ProfileReport #数据EDA交互工具 EDA（Exploratory Data Analysis）数据探索性分析

from sklearn.model_selection import train_test_split #切割数据集

from sklearn.linear_model import LogisticRegression #逻辑回归
from sklearn.ensemble import RandomForestClassifier #随机森林
from sklearn.svm import SVC, LinearSVC #支持向量机
from sklearn.neighbors import KNeighborsClassifier #k近邻
from sklearn.naive_bayes import GaussianNB #朴素贝叶斯
from sklearn.linear_model import Perceptron #感知机
from sklearn.linear_model import SGDClassifier #随机梯度下降
from sklearn.tree import DecisionTreeClassifier #决策树

from sklearn.preprocessing import LabelEncoder #分类变量编码

from sklearn.model_selection import cross_val_score #模型评估


df = pd.read_csv('data/train.csv')


# 查看缺失值
df.isna().sum()

PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64


# 对连续变量进行填充 用均值填充
df['Age'] = df['Age'].fillna(df['Age'].mean())


# 查看数据分布情况
df['Cabin'].value_counts()

B96 B98        4
G6             4
C23 C25 C27    4
C22 C26        3
F33            3
              ..
E34            1
C7             1
C54            1
E36            1
C148           1
Name: Cabin, Length: 147, dtype: int64


df['Embarked'].value_counts()

S    644
C    168
Q     77
Name: Embarked, dtype: int64


# 对分类变量进行填充 填充某个缺失值字符(NA)、用最多类别的进行填充
df['Cabin'] = df['Cabin'].fillna('NA')
df['Embarked'] = df['Embarked'].fillna('S')


# 查看重复值个数
df.duplicated().sum()

0


train_df = pd.read_csv('data/train.csv')
test_df = pd.read_csv('data/test.csv')
combine = [train_df, test_df]


train_df.columns

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')


train_df.describe()


train_df.describe(include=['O'])


print(train_df.isna().sum())
print('_'*40)
print(test_df.isna().sum())

PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64
________________________________________
PassengerId      0
Pclass           0
Name             0
Sex              0
Age             86
SibSp            0
Parch            0
Ticket           0
Fare             1
Cabin          327
Embarked         0
dtype: int64


grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', height=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=.5, bins=20)
grid.add_legend()

<seaborn.axisgrid.FacetGrid at 0x22eab933160>


for dataset in combine:
    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)


guess_ages = np.zeros((2,3))
for dataset in combine:
    for i in range(0, 2):
        for j in range(0, 3):
            guess_df = dataset[(dataset['Sex'] == i) & \
                (dataset['Pclass'] == j+1)]['Age'].dropna()

            age_guess = guess_df.median()

            # Convert random age float to nearest .5 age
            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5
            
    for i in range(0, 2):
        for j in range(0, 3):
            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\
                    'Age'] = guess_ages[i,j]

    dataset['Age'] = dataset['Age'].astype(int)


train_df['AgeBand'] = pd.cut(train_df['Age'], 5)
train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)


train_df.Fare

0       7.2500
1      71.2833
2       7.9250
3      53.1000
4       8.0500
        ...   
886    13.0000
887    30.0000
888    23.4500
889    30.0000
890     7.7500
Name: Fare, Length: 891, dtype: float64


df.head(3)


df.columns

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')


df.groupby(['Pclass','Survived'])['Survived'].count().unstack().plot(kind='bar',stacked='True')

<Axes: xlabel='Pclass'>


df.groupby(['Sex','Survived'])['Survived'].count().unstack().plot(kind='bar',stacked='True')

<Axes: xlabel='Sex'>


df.groupby(['SibSp','Survived'])['Survived'].count().unstack().plot(kind='bar',stacked='True')

<Axes: xlabel='SibSp'>


df.groupby(['Parch','Survived'])['Survived'].count().unstack().plot(kind='bar',stacked='True')

<Axes: xlabel='Parch'>


df.groupby(['Embarked','Survived'])['Survived'].count().unstack().plot(kind='bar',stacked='True')

<Axes: xlabel='Embarked'>


g = sns.FacetGrid(df, col='Survived')
g.map(plt.hist, 'Age', bins=20)

<seaborn.axisgrid.FacetGrid at 0x22ea580afb0>


facet = sns.FacetGrid(df, hue="Survived",aspect=3)
facet.map(sns.kdeplot,'Age',shade= True)
facet.set(xlim=(0, df['Age'].max()))
facet.add_legend()

c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\axisgrid.py:848: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  func(*plot_args, **plot_kwargs)
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\axisgrid.py:848: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  func(*plot_args, **plot_kwargs)

<seaborn.axisgrid.FacetGrid at 0x22e841f6a10>


facet = sns.FacetGrid(df, hue="Survived",aspect=3)
facet.map(sns.kdeplot,'Fare',shade= True)
facet.set(xlim=(0, df['Fare'].max()))
facet.add_legend()

c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\axisgrid.py:848: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  func(*plot_args, **plot_kwargs)
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\seaborn\axisgrid.py:848: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  func(*plot_args, **plot_kwargs)

<seaborn.axisgrid.FacetGrid at 0x1f39775c4f0>


sns.heatmap(df.corr(), annot=True, cmap='coolwarm', vmin=-1, vmax=1)

C:\Users\XLL\AppData\Local\Temp\ipykernel_11864\2675011510.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  sns.heatmap(df.corr(), annot=True, cmap='coolwarm', vmin=-1, vmax=1)

<Axes: >


profile = ProfileReport(df)
profile

Summarize dataset: 100%|██████████| 47/47 [00:05<00:00,  7.95it/s, Completed]                       
Generate report structure: 100%|██████████| 1/1 [00:05<00:00,  5.44s/it]
Render HTML: 100%|██████████| 1/1 [00:01<00:00,  1.58s/it]


for dataset in combine:
    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\.', expand=False)

pd.crosstab(train_df['Title'], train_df['Sex'])


for dataset in combine:
    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
 	'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')

    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
    
train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()


for dataset in combine:
    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1

train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)


for dataset in combine:
    dataset['IsAlone'] = 0
    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1

train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()


def model_score(X,y):
    # 对数据集进行切割
    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)
    lr = LogisticRegression()
    lr.fit(X_train, y_train)
    # 查看训练集和测试集score值
    print("Training set score: {:.2f}".format(lr.score(X_train, y_train)))
    print("Testing set score: {:.2f}".format(lr.score(X_test, y_test)))


# 取出所有的输入特征
data = df[['Pclass','Sex','Age','SibSp','Parch','Fare', 'Embarked']]
# 进行虚拟变量转换
data = pd.get_dummies(data)
data.head(1)


model_score(data,df['Survived'])

Training set score: 0.80
Testing set score: 0.78

c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


df = pd.read_csv('data/train_1.csv')
#分类变量编码
for feat in ['Sex', 'Embarked']:
    lbl = LabelEncoder()  
    label_dict = dict(zip(df[feat].unique(), range(df[feat].nunique())))
    df[feat + "_labelEncode"] = df[feat].map(label_dict)
    df[feat + "_labelEncode"] = lbl.fit_transform(df[feat].astype(str))
# 取出所有的输入特征
data = df[['Pclass','Sex_labelEncode','Age','SibSp','Parch','Fare', 'Embarked_labelEncode']]
data.head(1)


model_score(data,df['Survived'])

Training set score: 0.81
Testing set score: 0.79

c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


df = pd.read_csv('data/train_2.csv')
df['AgeBand'] = pd.cut(df['Age'], 5,labels = [1,2,3,4,5])
df.groupby(['AgeBand','Survived'])['Survived'].count().unstack().plot(kind='bar',stacked='True')

<Axes: xlabel='AgeBand'>


df['Fare'].describe()

count    891.000000
mean      32.204208
std       49.693429
min        0.000000
25%        7.910400
50%       14.454200
75%       31.000000
max      512.329200
Name: Fare, dtype: float64


df['FareBand'] = pd.cut(df['Fare'],[0,8,16,32,100,513],labels = [1,2,3,4,5])
df.groupby(['FareBand','Survived'])['Survived'].count().unstack().plot(kind='bar',stacked='True')

<Axes: xlabel='FareBand'>


data = df[['Pclass','Sex_labelEncode','AgeBand','SibSp','Parch','FareBand', 'Embarked_labelEncode']]
data.head(1)


model_score(data,df['Survived'])

Training set score: 0.62
Testing set score: 0.61

c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\arrays\base.py:513: RuntimeWarning: invalid value encountered in cast
  result = np.asarray(self, dtype=dtype)
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=2):
ABNORMAL_TERMINATION_IN_LNSRCH.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\arrays\base.py:513: RuntimeWarning: invalid value encountered in cast
  result = np.asarray(self, dtype=dtype)
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\arrays\base.py:513: RuntimeWarning: invalid value encountered in cast
  result = np.asarray(self, dtype=dtype)


def preprocessing(df):
    df['Age'] = df['Age'].fillna(df['Age'].mean())
    df['Fare'] = df['Fare'].fillna(df['Fare'].mean())
    df['Cabin'] = df['Cabin'].fillna('NA')
    df['Embarked'] = df['Embarked'].fillna('S')
    for feat in ['Sex', 'Embarked']:
        lbl = LabelEncoder()  
        label_dict = dict(zip(df[feat].unique(), range(df[feat].nunique())))
        df[feat + "_labelEncode"] = df[feat].map(label_dict)
        df[feat + "_labelEncode"] = lbl.fit_transform(df[feat].astype(str))
    data = df[['Pclass','Sex_labelEncode','Age','SibSp','Parch','Fare', 'Embarked_labelEncode']]
    return data


def training():
    df = pd.read_csv('data/train.csv')
    data = preprocessing(df)
    X=data
    y=df['Survived']
    X_train, X_test, y_train, y_test = train_test_split(X,y, stratify=y, random_state=0)
    lr = LogisticRegression()
    lr.fit(X_train, y_train)
    print("Training set score: {:.2f}".format(lr.score(X_train, y_train)))
    print("Testing set score: {:.2f}".format(lr.score(X_test, y_test)))
    return lr


lr = training()

Training set score: 0.81
Testing set score: 0.79

c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


test = pd.read_csv('data/test.csv')
test_data = preprocessing(test)
pred = lr.predict(test_data)
result = pd.DataFrame({'PassengerId':test['PassengerId'],'Survived':pred})
result.to_csv('data/result.csv',index=False)


def preprocessing(df):
    df['Age'] = df['Age'].fillna(df['Age'].mean())
    df['Fare'] = df['Fare'].fillna(df['Fare'].mean())
    df['Cabin'] = df['Cabin'].fillna('NA')
    df['Embarked'] = df['Embarked'].fillna('S')
    for feat in ['Sex', 'Embarked']:
        lbl = LabelEncoder()  
        label_dict = dict(zip(df[feat].unique(), range(df[feat].nunique())))
        df[feat + "_labelEncode"] = df[feat].map(label_dict)
        df[feat + "_labelEncode"] = lbl.fit_transform(df[feat].astype(str))
    df['AgeBand'] = pd.cut(df['Age'], 5,labels = [1,2,3,4,5])
    data = df[['Pclass','Sex_labelEncode','AgeBand','SibSp','Parch','Fare', 'Embarked_labelEncode']]
    return data


def training():
    df = pd.read_csv('data/train.csv')
    data = preprocessing(df)
    X=data
    y=df['Survived']
    X_train, X_test, y_train, y_test = train_test_split(X,y, stratify=y, random_state=0)
    lr = LogisticRegression()
    lr.fit(X_train, y_train)
    print("Training set score: {:.2f}".format(lr.score(X_train, y_train)))
    print("Testing set score: {:.2f}".format(lr.score(X_test, y_test)))
    return lr


lr = training()

Training set score: 0.80
Testing set score: 0.78

c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


test = pd.read_csv('data/test.csv')
test_data = preprocessing(test)
pred = lr.predict(test_data)
result = pd.DataFrame({'PassengerId':test['PassengerId'],'Survived':pred})
result.to_csv('data/result.csv',index=False)


def preprocessing(df):

    df['Fare'] = df['Fare'].fillna(df['Fare'].median())
    df['Cabin'] = df['Cabin'].fillna('NA')
    df['Embarked'] = df['Embarked'].fillna('S')

    df['Title'] = df.Name.str.extract(' ([A-Za-z]+)\.', expand=False)
    df['Title'] = df['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
 	'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
    df['Title'] = df['Title'].replace('Mlle', 'Miss')
    df['Title'] = df['Title'].replace('Ms', 'Miss')
    df['Title'] = df['Title'].replace('Mme', 'Mrs')

    df['FamilySize'] = df['SibSp'] + df['Parch'] + 1

    df['IsAlone'] = 0
    df.loc[df['FamilySize'] == 1, 'IsAlone'] = 1

    for feat in ['Sex', 'Embarked', 'Title']:
        lbl = LabelEncoder()  
        label_dict = dict(zip(df[feat].unique(), range(df[feat].nunique())))
        df[feat] = df[feat].map(label_dict)
        df[feat] = lbl.fit_transform(df[feat].astype(str))

    guess_ages = np.zeros((2,3))
    for i in range(0, 2):
        for j in range(0, 3):
            guess_df = df[(df['Sex'] == i) & \
                (df['Pclass'] == j+1)]['Age'].dropna()
            age_guess = guess_df.median()
            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5
    for i in range(0, 2):
        for j in range(0, 3):
            df.loc[ (df.Age.isnull()) & (df.Sex == i) & (df.Pclass == j+1),\
                    'Age'] = guess_ages[i,j]
    df['Age'] = df['Age'].astype(int)

    df['AgeBand'] = pd.cut(df['Age'], 5,labels = [1,2,3,4,5])

    data = df[['Pclass','Sex','AgeBand','SibSp','Parch','Fare','Embarked','Title','IsAlone']]
    
    return data


def training():
    df = pd.read_csv('data/train.csv')
    data = preprocessing(df)
    X=data
    y=df['Survived']
    lr = LogisticRegression()
    lr.fit(X,y)
    print("Training set score: {:.2f}".format(lr.score(X,y)))
    return lr


lr = training()

Training set score: 0.81

c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


test = pd.read_csv('data/test.csv')
test_data = preprocessing(test)
pred = lr.predict(test_data)
result = pd.DataFrame({'PassengerId':test['PassengerId'],'Survived':pred})
result.to_csv('data/result.csv',index=False)


def preprocessing(df):

    df['Fare'] = df['Fare'].fillna(df['Fare'].median())
    df['Cabin'] = df['Cabin'].fillna('NA')
    df['Embarked'] = df['Embarked'].fillna('S')

    df['Title'] = df.Name.str.extract(' ([A-Za-z]+)\.', expand=False)
    df['Title'] = df['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
 	'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
    df['Title'] = df['Title'].replace('Mlle', 'Miss')
    df['Title'] = df['Title'].replace('Ms', 'Miss')
    df['Title'] = df['Title'].replace('Mme', 'Mrs')

    df['FamilySize'] = df['SibSp'] + df['Parch'] + 1

    df['IsAlone'] = 0
    df.loc[df['FamilySize'] == 1, 'IsAlone'] = 1

    for feat in ['Sex', 'Embarked', 'Title']:
        lbl = LabelEncoder()  
        label_dict = dict(zip(df[feat].unique(), range(df[feat].nunique())))
        df[feat] = df[feat].map(label_dict)
        df[feat] = lbl.fit_transform(df[feat].astype(str))

    guess_ages = np.zeros((2,3))
    for i in range(0, 2):
        for j in range(0, 3):
            guess_df = df[(df['Sex'] == i) & \
                (df['Pclass'] == j+1)]['Age'].dropna()
            age_guess = guess_df.median()
            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5
    for i in range(0, 2):
        for j in range(0, 3):
            df.loc[ (df.Age.isnull()) & (df.Sex == i) & (df.Pclass == j+1),\
                    'Age'] = guess_ages[i,j]
    df['Age'] = df['Age'].astype(int)

    df['AgeBand'] = pd.cut(df['Age'], 5,labels = [1,2,3,4,5])

    df['Age*Class'] = df.Age * df.Pclass

    data = df[['Pclass','Sex','AgeBand','SibSp','Parch','Fare','Embarked','Title','IsAlone','Age*Class']]
    
    return data


def training():
    df = pd.read_csv('data/train.csv')
    data = preprocessing(df)
    X=data
    y=df['Survived']
    lr = RandomForestClassifier()
    lr.fit(X, y)
    print(cross_val_score(lr, X, y, cv=10).mean())
    return lr


lr = training()

0.8159925093632958


test = pd.read_csv('data/test.csv')
test_data = preprocessing(test)
pred = lr.predict(test_data)
result = pd.DataFrame({'PassengerId':test['PassengerId'],'Survived':pred})
result.to_csv('data/result.csv',index=False)


def preprocessing(df):

    df['Fare'] = df['Fare'].fillna(df['Fare'].median())
    df['Cabin'] = df['Cabin'].fillna('NA')
    df['Embarked'] = df['Embarked'].fillna('S')

    df['Title'] = df.Name.str.extract(' ([A-Za-z]+)\.', expand=False)
    df['Title'] = df['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
 	'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
    df['Title'] = df['Title'].replace('Mlle', 'Miss')
    df['Title'] = df['Title'].replace('Ms', 'Miss')
    df['Title'] = df['Title'].replace('Mme', 'Mrs')

    df['FamilySize'] = df['SibSp'] + df['Parch'] + 1

    df['IsAlone'] = 0
    df.loc[df['FamilySize'] == 1, 'IsAlone'] = 1

    for feat in ['Sex', 'Embarked', 'Title']:
        lbl = LabelEncoder()  
        label_dict = dict(zip(df[feat].unique(), range(df[feat].nunique())))
        df[feat] = df[feat].map(label_dict)
        df[feat] = lbl.fit_transform(df[feat].astype(str))

    guess_ages = np.zeros((2,3))
    for i in range(0, 2):
        for j in range(0, 3):
            guess_df = df[(df['Sex'] == i) & \
                (df['Pclass'] == j+1)]['Age'].dropna()
            age_guess = guess_df.median()
            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5
    for i in range(0, 2):
        for j in range(0, 3):
            df.loc[ (df.Age.isnull()) & (df.Sex == i) & (df.Pclass == j+1),\
                    'Age'] = guess_ages[i,j]
    df['Age'] = df['Age'].astype(int)

    df['AgeBand'] = pd.cut(df['Age'], 5,labels = [1,2,3,4,5])

    df['Age*Class'] = df.Age * df.Pclass

    data = df[['Pclass','Sex','AgeBand','SibSp','Parch','Fare','Embarked','Title','IsAlone','Age*Class']]
    
    return data


scores = pd.Series()

C:\Users\XLL\AppData\Local\Temp\ipykernel_772\3866007741.py:1: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  scores = pd.Series()


df = pd.read_csv('data/train.csv')
data = preprocessing(df)
X=data
y=df['Survived']

lr = LogisticRegression()
lr.fit(X, y)
scores['LogisticRegression'] = round(cross_val_score(lr, X, y, cv=10).mean()*100,2)

lr = SVC()
lr.fit(X, y)
scores['Support Vector Machines'] = round(cross_val_score(lr, X, y, cv=10).mean()*100,2)

lr = KNeighborsClassifier()
lr.fit(X, y)
scores['KNN'] = round(cross_val_score(lr, X, y, cv=10).mean()*100,2)

lr = GaussianNB()
lr.fit(X, y)
scores['Naive Bayes'] = round(cross_val_score(lr, X, y, cv=10).mean()*100,2)

lr = Perceptron()
lr.fit(X, y)
scores['Perceptron'] = round(cross_val_score(lr, X, y, cv=10).mean()*100,2)

lr = LinearSVC()
lr.fit(X, y)
scores['Linear Support Vector Machines'] = round(cross_val_score(lr, X, y, cv=10).mean()*100,2)

lr = SGDClassifier()
lr.fit(X, y)
scores['Stochastic Gradient Descent'] = round(cross_val_score(lr, X, y, cv=10).mean()*100,2)

lr = DecisionTreeClassifier()
lr.fit(X, y)
scores['Decision Tree'] = round(cross_val_score(lr, X, y, cv=10).mean()*100,2)

lr = RandomForestClassifier()
lr.fit(X, y)
scores['Random Forest'] = round(cross_val_score(lr, X, y, cv=10).mean()*100,2)

c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
c:\Users\XLL\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\svm\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(


models = scores.reset_index()
models.columns = ['model', 'score']
models = models.sort_values(by='score', ascending=False)
models

	AgeBand	Survived
0	(-0.08, 16.0]	0.550000
1	(16.0, 32.0]	0.337374
2	(32.0, 48.0]	0.412037
3	(48.0, 64.0]	0.434783
4	(64.0, 80.0]	0.090909

	FamilySize	Survived
3	4	0.724138
2	3	0.578431
1	2	0.552795
6	7	0.333333
0	1	0.303538
4	5	0.200000
5	6	0.136364
7	8	0.000000
8	11	0.000000

1.1 处理缺失值和重复值¶

法一：¶

法二：¶

1.2 数据可视化¶

1.3 创造新变量¶

2.1 模型搭建¶

2.1.1 分类变量用pd.get_dummies(data)¶

2.1.2 分类变量编码用LabelEncoder()¶

2.1.3 Age,Fare分箱¶

2.2 预测结果¶

2.2.1 Age均值填充，Age,Sex编码（ 0.76794 ）¶

2.2.2 Age均值填充，Age,Sex编码,Age改成Ageband（ 0.75358 ）¶

2.2.3 Age分类中位数填充，Name中提取称谓Title，isAlone（ 0.76555 ）¶

2.2.4 用RF，Age分类中位数填充，Name中提取称谓Title，isAlone（ 0.72488 ）¶

2.3 模型评估¶

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

	Name	Sex	Ticket	Cabin	Embarked
count	891	891	891	204	889
unique	891	2	681	147	3
top	Braund, Mr. Owen Harris	male	347082	B96 B98	S
freq	1	577	7	4	644

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NA	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NA	S

Sex	female	male
Title
Capt	0	1
Col	0	2
Countess	1	0
Don	0	1
Dr	1	6
Jonkheer	0	1
Lady	1	0
Major	0	2
Master	0	40
Miss	182	0
Mlle	2	0
Mme	1	0
Mr	0	517
Mrs	125	0
Ms	1	0
Rev	0	6
Sir	0	1

	Title	Survived
0	Master	0.575000
1	Miss	0.702703
2	Mr	0.156673
3	Mrs	0.793651
4	Rare	0.347826

	model	score
8	Random Forest	81.82
0	LogisticRegression	80.59
3	Naive Bayes	80.59
7	Decision Tree	79.13
6	Stochastic Gradient Descent	77.1
4	Perceptron	72.72
2	KNN	70.94
1	Support Vector Machines	70.5
5	Linear Support Vector Machines	66.77

	IsAlone	Survived
0	0	0.505650
1	1	0.303538

1.1 处理缺失值和重复值¶

法一：¶

法二：¶

1.2 数据可视化¶

1.3 创造新变量¶

2.1 模型搭建¶

2.1.1 分类变量 用pd.get_dummies(data)¶

2.1.2 分类变量编码 用LabelEncoder()¶

2.1.3 Age,Fare分箱¶

2.2 预测结果¶

2.2.1 Age均值填充，Age,Sex编码（ 0.76794 ）¶

2.2.2 Age均值填充，Age,Sex编码,Age改成Ageband（ 0.75358 ）¶

2.2.3 Age分类中位数填充，Name中提取称谓Title，isAlone（ 0.76555 ）¶

2.2.4 用RF，Age分类中位数填充，Name中提取称谓Title，isAlone（ 0.72488 ）¶

2.3 模型评估¶

2.1.1 分类变量用pd.get_dummies(data)¶

2.1.2 分类变量编码用LabelEncoder()¶